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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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PPE Detector for Employee Safety Free trial

Latest Version:
1.0
Real-time CV model for PPE non-compliance detection in manufacturing, construction, laboratory, and other industrial environments.

    Product Overview

    The PPE Detector for Employee Safety is a real-time computer vision model for identifying PPE non-compliance in working environments. The solution is a tool to ensure worker safety on building sites, fabrication lines, laboratories, steel, oil & gas enterprises, and other industrial environments where safety rules should be strictly followed. The solution is trained on the dataset manually selected and annotated by the VITechLab team. It detects the absence of any of the following objects on a person: Coat, Glasses, Glove, Mask, Helmet. It works with live streams from CCTV cameras. We also have a ready to use software, PPE Monitoring Platform: https://aws.amazon.com/marketplace/pp/B08BT5CV2F We provide free support during the trial period! After you've succeeded with the subscription, reach out at: support@vitechlab.com

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Trained on a synthetic dataset of 100,000 images and fine-tuned on the VITech Lab privately collected a dataset of real images from IP/CCTV cameras. The training dataset was considerably enlarged with augmented data. A synthetic dataset was collected with domain randomization to fit real images. The model was trained on images of 300x300 resolution and accepts images of any size that are resized internally.

    • Uses a custom designed in VITech object detection architecture to detect people and different equipment they wear. The inference time is dependent on the number of people detected in a single image.

    • Need a custom-made solution for video/image analysis? Or maybe need a custom PPE compliance detector? Reach us at support@vitechlab.com

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$5.00/hr

    running on ml.p3.2xlarge

    Model Batch Transform$20.00/hr

    running on ml.p3.2xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$3.825/host/hr

    running on ml.p3.2xlarge

    SageMaker Batch Transform$3.825/host/hr

    running on ml.p3.2xlarge

    About Free trial

    Try this product for 5 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.m4.4xlarge
    $5.00
    ml.m5.4xlarge
    $5.00
    ml.m5d.24xlarge
    $5.00
    ml.m4.16xlarge
    $5.00
    ml.m5.2xlarge
    $5.00
    ml.p3.16xlarge
    $5.00
    ml.c5d.4xlarge
    $5.00
    ml.m4.2xlarge
    $5.00
    ml.c5.2xlarge
    $5.00
    ml.c5d.9xlarge
    $5.00
    ml.p3.2xlarge
    Vendor Recommended
    $5.00
    ml.c4.2xlarge
    $5.00
    ml.m4.10xlarge
    $5.00
    ml.m5.24xlarge
    $5.00
    ml.m5d.xlarge
    $5.00
    ml.p2.xlarge
    $5.00
    ml.m5.12xlarge
    $5.00
    ml.m5d.4xlarge
    $5.00
    ml.p2.16xlarge
    $5.00
    ml.c4.4xlarge
    $5.00
    ml.m5.xlarge
    $5.00
    ml.c5.9xlarge
    $5.00
    ml.c5.4xlarge
    $5.00
    ml.m4.xlarge
    $5.00
    ml.m5d.2xlarge
    $5.00
    ml.p3.8xlarge
    $5.00
    ml.m5d.12xlarge
    $5.00
    ml.c4.8xlarge
    $5.00
    ml.p2.8xlarge
    $5.00
    ml.t2.xlarge
    $5.00
    ml.c5.18xlarge
    $5.00
    ml.c5d.18xlarge
    $5.00
    ml.t2.2xlarge
    $5.00
    ml.c5d.2xlarge
    $5.00

    Usage Information

    Fulfillment Methods

    Amazon SageMaker

    Supported content types: image/jpeg

    This model accepts images in the mime-type specified above.

    The image must be at least 300x300. The model resizes the image to the size specified by the user before performing the inference. Better results are achieved with 16:9 image proportions.

    Content type: text/json

    For every image, the model returns a single JSON file with all the detections.

    The model returns JSON object, that includes an array with individual elements for each face detected. Each element has three attributes:

    1. box_points: includes the bounding box around the detected face. Each bounding box consists of four numbers in [X1 Y1 X2 Y2] format in the source image coordinates.
    2. classes: dictionary with class names requested and probabilities. Probability score that the person in this bounding box has a requested item. Probability is given in percents (0..100 range)

    Prediction method can take additional parameter “detection-elements”: List of the classes that model is requested to detect in people: 'no_helmet', 'no_glasses', 'no_glove' - for manufacturing environment 'lab_no_coat', 'lab_no_glasses', 'lab_no_glove', 'lab_no_mask' - for healthcare environment

    We recommend using this model for real-time inference for better utilization of the endpoint. Optionally, batch transform is also available.

    You can find more details here: https://github.com/VITechLab/aws-sagemaker-examples/tree/master/General-Purpose-PPE-Detector

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    PPE Detector for Employee Safety

    If you have any issues or feature requests, please write to us, and we will be happy to help you as soon as possible. We can also create custom software and models optimised for your specific use case. Reach us at: support@vitechlab.com

    AWS Infrastructure

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

    Learn More

    Refund Policy

    We do not offer refunds at this time.

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